Privacy Safe Synthetic Data for Healthcare and Finance AI

The data problem is quantitative, too

Real-world studies show that many healthcare datasets are under 10,000 labeled examples after de-identification, yet leading medical imaging models often need hundreds of thousands of examples to stabilize training. In finance, transaction-level data used for fraud detection or credit scoring is similarly sparse, and global banks routinely reject data-sharing proposals because of privacy concerns. This squeeze forces teams to rely on small, noisy datasets or expensive, slow data partnerships.

What synthetic data actually solves

• ●Synthetic data mirrors the statistical structure of real records: marginal distributions, correlations, and rare events can be preserved with high fidelity.
• ●Because synthetic records contain no real individuals, they can be used freely for training and evaluation without additional consent or legal review.
• ●You can generate large volumes of labeled examples for underrepresented populations or rare conditions, improving model generalization.
• ●Regulatory scrutiny is reduced: regulators focus on the data generation process and controls rather than the raw inputs themselves.

Techniques that matter in regulated domains

Modern synthetic data pipelines for healthcare and finance combine several methods. GANs and diffusion models are trained on de-identified real data to learn joint distributions of features like lab values, diagnoses, or transaction categories. This modeling step is followed by rigorous validation: statistical tests, machine learning classifiers, and expert review to ensure synthetic data is not just random but realistically complex. For structured tabular fields, constrained generation enforces domain constraints such as correct units, valid ranges, and consistent relationships between related variables.

How Coasty fits

Coasty takes a different approach. Their system runs computer use agents on real desktops and browsers, collecting realistic interaction data across workflows. This ground-truth behavior can be used to build synthetic datasets and trajectories that reflect how users actually interact with healthcare portals, banking apps, or other regulated systems. Coasty’s offering is a custom synthetic data service: you talk directly with the team to define requirements, and they produce tailored datasets that match your use cases and compliance standards. There is no self-serve dashboard, no fixed packages, and no public pricing.